| Literature DB >> 32658651 |
Tanujit Dey1, Anish Mukherjee2, Sounak Chakraborty3.
Abstract
Survival (time-to-event) analysis is commonly used in clinical research. Key features of performing a survival analysis include checking proportional hazards assumptions, reporting CIs for hazards ratios and relative risks, graphically displaying the findings, and analyzing with consideration of competing risks. This article provides a brief overview of important statistical considerations for survival analysis. Censoring schemes, different methods of survival function estimation, and ways to compare survival curves are described. We also explain competing risk and how to model survival data in the presence of it. Different kinds of bias that influence survival estimation and avenues to model the data under these circumstances are also described. Several analysis techniques are accompanied by graphical representations illustrating proper reporting strategies. We provide a list of guiding statements for researchers and reviewers.Keywords: Cox proportional hazards model; Kaplan-Meier estimate; accelerated failure time model; censoring; competing risk; hazard function; log-rank test; survival function
Mesh:
Year: 2020 PMID: 32658651 DOI: 10.1016/j.chest.2020.03.015
Source DB: PubMed Journal: Chest ISSN: 0012-3692 Impact factor: 9.410